Field of the Invention
Embodiments of the present invention relate generally to network architecture and semantics for distributed processing on a data pipeline, and, more specifically, to distributed smart grid processing.
Description of the Related Art
A conventional electricity distribution infrastructure typically includes a plurality of energy consumers, such as houses, business, and so forth, coupled to a grid of intermediate distribution entities, such as transformers, feeders, substations, etc. The grid of distribution entities draws power from upstream power plants and distributes that power to the downstream consumers. In a modern electricity distribution infrastructure, the consumers, as well as the intermediate distribution entities, sometimes include “smart” meters and other monitoring hardware coupled together to form a mesh network. The smart meters and other measurement and control devices collect data that reflects the operating state of the grid, as well as consumption and utilization of the grid, and then report the collected data, via the mesh network, to a centralized grid management facility, often referred to as the “back office.” Such a configuration is commonly known as a “smart grid.”
In a conventional smart grid, the back office receives a multitude of real-time data from the various smart meters and processes that data to identify specific operating conditions associated with the grid. Those conditions may include electrical events, such as sags or swells, as well as physical events, such as downed power lines or overloaded transformers, among other possibilities. The back office usually includes centralized processing hardware, such as a server room or datacenter, configured to process the smart meter data.
One problem with approach described above is that, with the expansion of smart grid infrastructure, the amount of data that must be transmitted to the back office for processing is growing quickly. Consequently, the network across which the smart meters transmit data can become quickly over-burdened with traffic and, therefore, suffer from throughput and latency issues. In addition, the processing hardware implemented by the back office may quickly become too slow, and therefore obsolete, as the amount of data that must be processed continues to grow in response to increased demand. As a general matter, the infrastructure required to transport and process data generated by a smart grid cannot scale nearly as quickly as the amount of data that is generated by the smart grid system.
As the foregoing illustrates, what is needed in the art is a more effective approach for transporting and processing data within large-scale network architectures.